A Wireless Sensor-Brain Interface System for Tracking and Guiding Animal Behaviors Through Closed-Loop Neuromodulation in Water Mazes
Bibliographic record
Abstract
This article presents a wireless sensor-brain interface (SBI) system designed for closed-loop neuromodulation in freely behaving animals. The system enables a novel experiment in which swimming rats navigate a water maze guided exclusively by neural stimulation. The system consists of two wirelessly linked application-specific integrated circuits (ASICs): a bidirectional neural interface and an animal-tracking image sensor. The neural interface ASIC features a novel stimulator design with adaptive termination-based charge-balancing and an energy-efficient neural recording front end that rejects stimulation artifacts. The animal-tracking image sensor extracts the swimming features of rats and maps them into stimulation parameters with hardware acceleration. Low-power ultra-wideband transceivers with a custom protocol have been developed to ensure reliable wireless communication between devices with a short latency, which is critical to closed-loop neuromodulation. Wireless power transfer and near-field data communication have been developed to allow a part of the neural interface to be implanted beneath the skin, eliminating the need for through-skin connectors. Both ASICs were fabricated using 180-nm CMOS technology, fully characterized on the bench, and have been successfully used in animal experiments. Through rigorous testing, the system demonstrated consistent success in guiding multiple rats through the water maze using only stimulation. This work advances closed-loop neuromodulation technology and also serves as a promising platform for future neuroscientific investigations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".